Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations34059
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 MiB
Average record size in memory88.0 B

Variable types

Numeric9
Categorical2

Alerts

Prediction has 3313 (9.7%) zeros Zeros

Reproduction

Analysis started2025-03-04 18:16:07.563411
Analysis finished2025-03-04 18:16:12.929959
Duration5.37 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Atmospheric Density
Real number (ℝ)

Distinct34056
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.011846976
Minimum-4.2833095
Maximum9.3240185
Zeros0
Zeros (%)0.0%
Negative18309
Negative (%)53.8%
Memory size266.2 KiB
2025-03-04T23:46:13.181725image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-4.2833095
5-th percentile-3.5125799
Q1-1.5609687
median-0.19924563
Q31.2919441
95-th percentile4.2021647
Maximum9.3240185
Range13.607328
Interquartile range (IQR)2.8529129

Descriptive statistics

Standard deviation2.2571073
Coefficient of variation (CV)-190.52182
Kurtosis0.30112795
Mean-0.011846976
Median Absolute Deviation (MAD)1.4234524
Skewness0.57880168
Sum-403.49614
Variance5.0945335
MonotonicityNot monotonic
2025-03-04T23:46:13.257941image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.5392594 2
 
< 0.1%
1.4309664 2
 
< 0.1%
-1.272124 2
 
< 0.1%
-3.5439434 1
 
< 0.1%
1.5346771 1
 
< 0.1%
0.66535795 1
 
< 0.1%
-0.57894593 1
 
< 0.1%
-1.6434073 1
 
< 0.1%
-1.3078111 1
 
< 0.1%
4.9395022 1
 
< 0.1%
Other values (34046) 34046
> 99.9%
ValueCountFrequency (%)
-4.2833095 1
< 0.1%
-4.276285 1
< 0.1%
-4.2444158 1
< 0.1%
-4.2377977 1
< 0.1%
-4.2361345 1
< 0.1%
-4.2354474 1
< 0.1%
-4.23329 1
< 0.1%
-4.1702404 1
< 0.1%
-4.1646767 1
< 0.1%
-4.163205 1
< 0.1%
ValueCountFrequency (%)
9.3240185 1
< 0.1%
9.037439 1
< 0.1%
9.005384 1
< 0.1%
8.881861 1
< 0.1%
8.859731 1
< 0.1%
8.782395 1
< 0.1%
8.765196 1
< 0.1%
8.636687 1
< 0.1%
8.633962 1
< 0.1%
8.568245 1
< 0.1%

Surface Temperature
Real number (ℝ)

Distinct34050
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0027038242
Minimum-5.426189
Maximum5.638094
Zeros0
Zeros (%)0.0%
Negative19143
Negative (%)56.2%
Memory size266.2 KiB
2025-03-04T23:46:13.369833image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-5.426189
5-th percentile-2.8625692
Q1-1.4238442
median-0.32962894
Q31.4985578
95-th percentile3.3940781
Maximum5.638094
Range11.064283
Interquartile range (IQR)2.922402

Descriptive statistics

Standard deviation1.9361632
Coefficient of variation (CV)-716.08323
Kurtosis-0.67243782
Mean-0.0027038242
Median Absolute Deviation (MAD)1.3615331
Skewness0.28698184
Sum-92.08955
Variance3.7487279
MonotonicityNot monotonic
2025-03-04T23:46:13.449705image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.2676 2
 
< 0.1%
3.1247435 2
 
< 0.1%
-1.5454339 2
 
< 0.1%
0.6186842 2
 
< 0.1%
-0.83189386 2
 
< 0.1%
-2.6393762 2
 
< 0.1%
-1.5627917 2
 
< 0.1%
-0.7923264 2
 
< 0.1%
-1.9423126 2
 
< 0.1%
-2.0139294 1
 
< 0.1%
Other values (34040) 34040
99.9%
ValueCountFrequency (%)
-5.426189 1
< 0.1%
-5.324749 1
< 0.1%
-5.280104 1
< 0.1%
-5.2703485 1
< 0.1%
-5.252889 1
< 0.1%
-5.2306385 1
< 0.1%
-5.142001 1
< 0.1%
-5.111298 1
< 0.1%
-5.0573435 1
< 0.1%
-5.036451 1
< 0.1%
ValueCountFrequency (%)
5.638094 1
< 0.1%
5.431874 1
< 0.1%
5.257284 1
< 0.1%
5.2437644 1
< 0.1%
5.2218986 1
< 0.1%
5.1523094 1
< 0.1%
5.127123 1
< 0.1%
5.106978 1
< 0.1%
5.071699 1
< 0.1%
5.047569 1
< 0.1%

Gravity
Real number (ℝ)

Distinct34052
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0039703462
Minimum-5.5538774
Maximum6.0302896
Zeros0
Zeros (%)0.0%
Negative16610
Negative (%)48.8%
Memory size266.2 KiB
2025-03-04T23:46:13.590870image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-5.5538774
5-th percentile-2.9835706
Q1-1.2764647
median0.05322854
Q31.2631027
95-th percentile2.9460848
Maximum6.0302896
Range11.584167
Interquartile range (IQR)2.5395674

Descriptive statistics

Standard deviation1.8039406
Coefficient of variation (CV)454.35347
Kurtosis-0.36641108
Mean0.0039703462
Median Absolute Deviation (MAD)1.2690665
Skewness-0.039307254
Sum135.22602
Variance3.2542016
MonotonicityNot monotonic
2025-03-04T23:46:13.700270image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2023059 2
 
< 0.1%
-2.281435 2
 
< 0.1%
2.67935 2
 
< 0.1%
2.3885822 2
 
< 0.1%
2.047897 2
 
< 0.1%
-2.361987 2
 
< 0.1%
-1.5314468 2
 
< 0.1%
2.3722498 1
 
< 0.1%
-1.797269 1
 
< 0.1%
0.025086416 1
 
< 0.1%
Other values (34042) 34042
> 99.9%
ValueCountFrequency (%)
-5.5538774 1
< 0.1%
-5.486238 1
< 0.1%
-5.4851193 1
< 0.1%
-5.3183813 1
< 0.1%
-5.317685 1
< 0.1%
-5.302247 1
< 0.1%
-5.2636623 1
< 0.1%
-5.2594576 1
< 0.1%
-5.1418347 1
< 0.1%
-5.1183143 1
< 0.1%
ValueCountFrequency (%)
6.0302896 1
< 0.1%
5.8399043 1
< 0.1%
5.834548 1
< 0.1%
5.7330194 1
< 0.1%
5.6321597 1
< 0.1%
5.526921 1
< 0.1%
5.4653783 1
< 0.1%
5.403297 1
< 0.1%
5.399485 1
< 0.1%
5.356745 1
< 0.1%

Water Content
Real number (ℝ)

Distinct34055
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0030033205
Minimum-5.816755
Maximum6.2870445
Zeros0
Zeros (%)0.0%
Negative16991
Negative (%)49.9%
Memory size266.2 KiB
2025-03-04T23:46:13.789046image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-5.816755
5-th percentile-2.6486992
Q1-1.2182536
median0.0034679119
Q31.0627348
95-th percentile2.9150096
Maximum6.2870445
Range12.1038
Interquartile range (IQR)2.2809884

Descriptive statistics

Standard deviation1.6887111
Coefficient of variation (CV)562.28134
Kurtosis-0.15125029
Mean0.0030033205
Median Absolute Deviation (MAD)1.1476516
Skewness0.17555574
Sum102.29009
Variance2.8517451
MonotonicityNot monotonic
2025-03-04T23:46:13.865115image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9332648 2
 
< 0.1%
-1.2945805 2
 
< 0.1%
1.9320487 2
 
< 0.1%
-0.86360425 2
 
< 0.1%
-0.29834202 1
 
< 0.1%
-1.9186189 1
 
< 0.1%
-2.7426498 1
 
< 0.1%
0.68361425 1
 
< 0.1%
-0.64073014 1
 
< 0.1%
-0.14084208 1
 
< 0.1%
Other values (34045) 34045
> 99.9%
ValueCountFrequency (%)
-5.816755 1
< 0.1%
-5.798147 1
< 0.1%
-5.3001866 1
< 0.1%
-5.243235 1
< 0.1%
-5.2160144 1
< 0.1%
-5.2034826 1
< 0.1%
-5.2028575 1
< 0.1%
-5.192955 1
< 0.1%
-5.1554337 1
< 0.1%
-5.1543546 1
< 0.1%
ValueCountFrequency (%)
6.2870445 1
< 0.1%
5.987579 1
< 0.1%
5.7981014 1
< 0.1%
5.789329 1
< 0.1%
5.776071 1
< 0.1%
5.7756133 1
< 0.1%
5.7426233 1
< 0.1%
5.712038 1
< 0.1%
5.6503606 1
< 0.1%
5.6494164 1
< 0.1%

Mineral Abundance
Real number (ℝ)

Distinct34053
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0059058663
Minimum-5.0773635
Maximum5.3355374
Zeros0
Zeros (%)0.0%
Negative16585
Negative (%)48.7%
Memory size266.2 KiB
2025-03-04T23:46:13.924344image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-5.0773635
5-th percentile-2.7093206
Q1-1.0693765
median0.05143214
Q31.1012126
95-th percentile2.5922637
Maximum5.3355374
Range10.412901
Interquartile range (IQR)2.1705891

Descriptive statistics

Standard deviation1.6020937
Coefficient of variation (CV)271.27159
Kurtosis-0.096841235
Mean0.0059058663
Median Absolute Deviation (MAD)1.0856954
Skewness-0.10260333
Sum201.1479
Variance2.5667043
MonotonicityNot monotonic
2025-03-04T23:46:13.988838image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9319828 2
 
< 0.1%
-1.667244 2
 
< 0.1%
-0.9453179 2
 
< 0.1%
-2.0321355 2
 
< 0.1%
2.361134 2
 
< 0.1%
0.14641404 2
 
< 0.1%
1.3432546 1
 
< 0.1%
2.1773193 1
 
< 0.1%
-0.571059 1
 
< 0.1%
0.058063358 1
 
< 0.1%
Other values (34043) 34043
> 99.9%
ValueCountFrequency (%)
-5.0773635 1
< 0.1%
-4.935731 1
< 0.1%
-4.859624 1
< 0.1%
-4.76619 1
< 0.1%
-4.7646756 1
< 0.1%
-4.747044 1
< 0.1%
-4.724135 1
< 0.1%
-4.720601 1
< 0.1%
-4.7189555 1
< 0.1%
-4.7107105 1
< 0.1%
ValueCountFrequency (%)
5.3355374 1
< 0.1%
5.3091884 1
< 0.1%
5.297156 1
< 0.1%
5.2198315 1
< 0.1%
5.214449 1
< 0.1%
5.1972427 1
< 0.1%
5.1865864 1
< 0.1%
5.073011 1
< 0.1%
4.9926815 1
< 0.1%
4.9665627 1
< 0.1%

Orbital Period
Real number (ℝ)

Distinct34050
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0019327718
Minimum-4.8010464
Maximum5.1110144
Zeros0
Zeros (%)0.0%
Negative16622
Negative (%)48.8%
Memory size266.2 KiB
2025-03-04T23:46:14.059211image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-4.8010464
5-th percentile-2.5696332
Q1-1.0542187
median0.047482815
Q31.064815
95-th percentile2.4367824
Maximum5.1110144
Range9.9120608
Interquartile range (IQR)2.1190338

Descriptive statistics

Standard deviation1.5143557
Coefficient of variation (CV)-783.51501
Kurtosis-0.28075567
Mean-0.0019327718
Median Absolute Deviation (MAD)1.0579158
Skewness-0.11072177
Sum-65.828276
Variance2.2932733
MonotonicityNot monotonic
2025-03-04T23:46:14.120181image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6134673 2
 
< 0.1%
-1.9493265 2
 
< 0.1%
-1.3179334 2
 
< 0.1%
0.7048577 2
 
< 0.1%
0.656943 2
 
< 0.1%
0.84553987 2
 
< 0.1%
-2.1414433 2
 
< 0.1%
2.4922962 2
 
< 0.1%
1.4822406 2
 
< 0.1%
-1.8181818 1
 
< 0.1%
Other values (34040) 34040
99.9%
ValueCountFrequency (%)
-4.8010464 1
< 0.1%
-4.8004346 1
< 0.1%
-4.7645693 1
< 0.1%
-4.7153163 1
< 0.1%
-4.686831 1
< 0.1%
-4.6466084 1
< 0.1%
-4.6391683 1
< 0.1%
-4.5344834 1
< 0.1%
-4.507785 1
< 0.1%
-4.5061154 1
< 0.1%
ValueCountFrequency (%)
5.1110144 1
< 0.1%
5.0115924 1
< 0.1%
4.7755356 1
< 0.1%
4.690596 1
< 0.1%
4.6528287 1
< 0.1%
4.5872636 1
< 0.1%
4.560671 1
< 0.1%
4.551911 1
< 0.1%
4.510819 1
< 0.1%
4.4389305 1
< 0.1%

Proximity to Star
Real number (ℝ)

Distinct34048
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0056764623
Minimum-4.5371866
Maximum4.731871
Zeros0
Zeros (%)0.0%
Negative17682
Negative (%)51.9%
Memory size266.2 KiB
2025-03-04T23:46:14.180356image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-4.5371866
5-th percentile-2.07517
Q1-0.9428217
median-0.06808486
Q30.87958468
95-th percentile2.2456771
Maximum4.731871
Range9.2690576
Interquartile range (IQR)1.8224064

Descriptive statistics

Standard deviation1.3175873
Coefficient of variation (CV)-232.11416
Kurtosis-0.16465069
Mean-0.0056764623
Median Absolute Deviation (MAD)0.90775324
Skewness0.20742336
Sum-193.33463
Variance1.7360362
MonotonicityNot monotonic
2025-03-04T23:46:14.241199image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.40229568 2
 
< 0.1%
-1.548242 2
 
< 0.1%
1.1583484 2
 
< 0.1%
-1.4078016 2
 
< 0.1%
0.67362744 2
 
< 0.1%
-0.7475407 2
 
< 0.1%
-1.0235691 2
 
< 0.1%
-0.6916682 2
 
< 0.1%
1.5085113 2
 
< 0.1%
1.9227643 2
 
< 0.1%
Other values (34038) 34039
99.9%
ValueCountFrequency (%)
-4.5371866 1
< 0.1%
-4.162187 1
< 0.1%
-3.9413304 1
< 0.1%
-3.8702376 1
< 0.1%
-3.8616724 1
< 0.1%
-3.812349 1
< 0.1%
-3.7863443 1
< 0.1%
-3.7712777 1
< 0.1%
-3.7541459 1
< 0.1%
-3.7375205 1
< 0.1%
ValueCountFrequency (%)
4.731871 1
< 0.1%
4.6967626 1
< 0.1%
4.6626744 1
< 0.1%
4.632862 1
< 0.1%
4.5826097 1
< 0.1%
4.569558 1
< 0.1%
4.5560837 1
< 0.1%
4.420982 1
< 0.1%
4.362626 1
< 0.1%
4.3468814 1
< 0.1%
Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size266.2 KiB
Category_8
5349 
Category_9
5342 
Category_10
4756 
Category_7
3671 
Category_11
3653 
Other values (15)
11288 

Length

Max length11
Median length10
Mean length10.458939
Min length10

Characters and Unicode

Total characters356221
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCategory_6
2nd rowCategory_8
3rd rowCategory_8
4th rowCategory_14
5th rowCategory_13

Common Values

ValueCountFrequency (%)
Category_8 5349
15.7%
Category_9 5342
15.7%
Category_10 4756
14.0%
Category_7 3671
10.8%
Category_11 3653
10.7%
Category_12 2724
8.0%
Category_6 2101
 
6.2%
Category_13 1890
 
5.5%
Category_14 1269
 
3.7%
Category_5 1188
 
3.5%
Other values (10) 2116
 
6.2%

Length

2025-03-04T23:46:14.297555image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
category_8 5349
15.7%
category_9 5342
15.7%
category_10 4756
14.0%
category_7 3671
10.8%
category_11 3653
10.7%
category_12 2724
8.0%
category_6 2101
 
6.2%
category_13 1890
 
5.5%
category_14 1269
 
3.7%
category_5 1188
 
3.5%
Other values (10) 2116
 
6.2%

Most occurring characters

ValueCountFrequency (%)
C 34059
9.6%
t 34059
9.6%
e 34059
9.6%
g 34059
9.6%
o 34059
9.6%
r 34059
9.6%
y 34059
9.6%
_ 34059
9.6%
a 34059
9.6%
1 19288
5.4%
Other values (9) 30402
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 356221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 34059
9.6%
t 34059
9.6%
e 34059
9.6%
g 34059
9.6%
o 34059
9.6%
r 34059
9.6%
y 34059
9.6%
_ 34059
9.6%
a 34059
9.6%
1 19288
5.4%
Other values (9) 30402
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 356221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 34059
9.6%
t 34059
9.6%
e 34059
9.6%
g 34059
9.6%
o 34059
9.6%
r 34059
9.6%
y 34059
9.6%
_ 34059
9.6%
a 34059
9.6%
1 19288
5.4%
Other values (9) 30402
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 356221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 34059
9.6%
t 34059
9.6%
e 34059
9.6%
g 34059
9.6%
o 34059
9.6%
r 34059
9.6%
y 34059
9.6%
_ 34059
9.6%
a 34059
9.6%
1 19288
5.4%
Other values (9) 30402
8.5%

Radiation Levels
Categorical

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size266.2 KiB
Category_8
5915 
Category_9
5698 
Category_7
5043 
Category_10
4871 
Category_11
3612 
Other values (15)
8920 

Length

Max length11
Median length10
Mean length10.36152
Min length10

Characters and Unicode

Total characters352903
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCategory_10
2nd rowCategory_7
3rd rowCategory_7
4th rowCategory_8
5th rowCategory_12

Common Values

ValueCountFrequency (%)
Category_8 5915
17.4%
Category_9 5698
16.7%
Category_7 5043
14.8%
Category_10 4871
14.3%
Category_11 3612
10.6%
Category_6 3048
8.9%
Category_12 2136
 
6.3%
Category_5 1339
 
3.9%
Category_13 1057
 
3.1%
Category_4 515
 
1.5%
Other values (10) 825
 
2.4%

Length

2025-03-04T23:46:14.343835image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
category_8 5915
17.4%
category_9 5698
16.7%
category_7 5043
14.8%
category_10 4871
14.3%
category_11 3612
10.6%
category_6 3048
8.9%
category_12 2136
 
6.3%
category_5 1339
 
3.9%
category_13 1057
 
3.1%
category_4 515
 
1.5%
Other values (10) 825
 
2.4%

Most occurring characters

ValueCountFrequency (%)
C 34059
9.7%
t 34059
9.7%
e 34059
9.7%
g 34059
9.7%
o 34059
9.7%
r 34059
9.7%
y 34059
9.7%
_ 34059
9.7%
a 34059
9.7%
1 15928
4.5%
Other values (9) 30444
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 352903
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 34059
9.7%
t 34059
9.7%
e 34059
9.7%
g 34059
9.7%
o 34059
9.7%
r 34059
9.7%
y 34059
9.7%
_ 34059
9.7%
a 34059
9.7%
1 15928
4.5%
Other values (9) 30444
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 352903
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 34059
9.7%
t 34059
9.7%
e 34059
9.7%
g 34059
9.7%
o 34059
9.7%
r 34059
9.7%
y 34059
9.7%
_ 34059
9.7%
a 34059
9.7%
1 15928
4.5%
Other values (9) 30444
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 352903
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 34059
9.7%
t 34059
9.7%
e 34059
9.7%
g 34059
9.7%
o 34059
9.7%
r 34059
9.7%
y 34059
9.7%
_ 34059
9.7%
a 34059
9.7%
1 15928
4.5%
Other values (9) 30444
8.6%
Distinct34046
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0044308463
Minimum-4.007504
Maximum3.8525667
Zeros0
Zeros (%)0.0%
Negative16360
Negative (%)48.0%
Memory size266.2 KiB
2025-03-04T23:46:14.394806image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-4.007504
5-th percentile-1.9798005
Q1-0.71325915
median0.0507581
Q30.79935265
95-th percentile1.7584517
Maximum3.8525667
Range7.8600707
Interquartile range (IQR)1.5126118

Descriptive statistics

Standard deviation1.1253026
Coefficient of variation (CV)253.97013
Kurtosis-0.1592744
Mean0.0044308463
Median Absolute Deviation (MAD)0.75467726
Skewness-0.21975033
Sum150.91019
Variance1.266306
MonotonicityNot monotonic
2025-03-04T23:46:14.552740image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.77379596 2
 
< 0.1%
-0.8435347 2
 
< 0.1%
-0.6738861 2
 
< 0.1%
0.74860716 2
 
< 0.1%
-0.7635946 2
 
< 0.1%
-0.0870384 2
 
< 0.1%
0.31833825 2
 
< 0.1%
0.82712585 2
 
< 0.1%
1.4671121 2
 
< 0.1%
0.8673846 2
 
< 0.1%
Other values (34036) 34039
99.9%
ValueCountFrequency (%)
-4.007504 1
< 0.1%
-3.9279456 1
< 0.1%
-3.9086783 1
< 0.1%
-3.8585846 1
< 0.1%
-3.6878495 1
< 0.1%
-3.6859965 1
< 0.1%
-3.6623535 1
< 0.1%
-3.5992885 1
< 0.1%
-3.5892086 1
< 0.1%
-3.58545 1
< 0.1%
ValueCountFrequency (%)
3.8525667 1
< 0.1%
3.7423236 1
< 0.1%
3.6385372 1
< 0.1%
3.6145012 1
< 0.1%
3.5902178 1
< 0.1%
3.5640156 1
< 0.1%
3.5385554 1
< 0.1%
3.5359325 1
< 0.1%
3.5047922 1
< 0.1%
3.5040174 1
< 0.1%

Prediction
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4579994
Minimum0
Maximum9
Zeros3313
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size266.2 KiB
2025-03-04T23:46:14.600565image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.890949
Coefficient of variation (CV)0.64848574
Kurtosis-1.2559993
Mean4.4579994
Median Absolute Deviation (MAD)3
Skewness0.030322363
Sum151835
Variance8.3575861
MonotonicityNot monotonic
2025-03-04T23:46:14.636328image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 3852
11.3%
3 3502
10.3%
7 3483
10.2%
9 3430
10.1%
2 3409
10.0%
6 3382
9.9%
8 3332
9.8%
4 3320
9.7%
0 3313
9.7%
5 3036
8.9%
ValueCountFrequency (%)
0 3313
9.7%
1 3852
11.3%
2 3409
10.0%
3 3502
10.3%
4 3320
9.7%
5 3036
8.9%
6 3382
9.9%
7 3483
10.2%
8 3332
9.8%
9 3430
10.1%
ValueCountFrequency (%)
9 3430
10.1%
8 3332
9.8%
7 3483
10.2%
6 3382
9.9%
5 3036
8.9%
4 3320
9.7%
3 3502
10.3%
2 3409
10.0%
1 3852
11.3%
0 3313
9.7%

Interactions

2025-03-04T23:46:12.222241image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:08.468533image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:08.949471image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.392148image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.886324image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.343420image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.876410image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.312297image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.738958image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.272420image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:08.528960image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.002086image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.444196image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.946539image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.392351image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.925857image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.361526image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.794357image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.318233image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:08.581044image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.048656image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.492334image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.997234image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.440870image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.975346image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.409252image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.847465image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.365410image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:08.634031image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.100382image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.541280image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.048373image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.594755image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.028959image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.455695image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.901143image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.414998image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:08.686803image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.149389image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.589451image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.097731image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.644027image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.080190image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.505002image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.961082image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.479035image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:08.740153image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.196368image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.638696image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.146176image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.689033image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.125226image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.549667image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.011619image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.528122image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:08.793787image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.244078image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.686553image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.193437image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.735045image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.170602image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.596884image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.063957image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.573419image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:08.843004image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.290826image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.733737image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.242096image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.780608image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.217047image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.643037image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.114546image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.625128image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:08.900240image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.345607image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:09.835277image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.295520image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:10.831242image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.267064image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:11.692950image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-04T23:46:12.171948image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-03-04T23:46:14.675942image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Atmospheric Composition IndexAtmospheric DensityGravityMagnetic Field StrengthMineral AbundanceOrbital PeriodPredictionProximity to StarRadiation LevelsSurface TemperatureWater Content
Atmospheric Composition Index1.000-0.029-0.0520.0420.022-0.010-0.082-0.0230.047-0.0060.015
Atmospheric Density-0.0291.000-0.0390.1540.129-0.007-0.094-0.0480.0970.0280.006
Gravity-0.052-0.0391.0000.0820.0160.030-0.0800.0040.092-0.009-0.002
Magnetic Field Strength0.0420.1540.0821.0000.0810.0610.2120.0620.0430.0960.083
Mineral Abundance0.0220.1290.0160.0811.0000.0460.313-0.0290.0620.005-0.007
Orbital Period-0.010-0.0070.0300.0610.0461.000-0.039-0.0200.063-0.0200.022
Prediction-0.082-0.094-0.0800.2120.313-0.0391.000-0.0040.1310.490-0.184
Proximity to Star-0.023-0.0480.0040.062-0.029-0.020-0.0041.0000.041-0.002-0.020
Radiation Levels0.0470.0970.0920.0430.0620.0630.1310.0411.0000.0790.044
Surface Temperature-0.0060.028-0.0090.0960.005-0.0200.490-0.0020.0791.0000.004
Water Content0.0150.006-0.0020.083-0.0070.022-0.184-0.0200.0440.0041.000

Missing values

2025-03-04T23:46:12.695185image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-04T23:46:12.774663image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Atmospheric DensitySurface TemperatureGravityWater ContentMineral AbundanceOrbital PeriodProximity to StarMagnetic Field StrengthRadiation LevelsAtmospheric Composition IndexPrediction
0-1.4594262.8902680.148757-0.8044390.4948750.044910-0.438796Category_6Category_100.4079419.0
1-2.971646-0.648251-0.9158590.255504-0.537165-2.0722511.355523Category_8Category_71.8762321.0
2-3.306354-0.316716-0.4312640.389815-1.961216-1.5101820.538593Category_8Category_70.9340551.0
3-0.752712-2.492542-1.072433-2.5617341.158838-1.2626381.447444Category_14Category_80.7260093.0
41.129258-3.333453-4.423914-1.0204090.711290-0.6067840.047264Category_13Category_12-1.6305043.0
5-3.492167-0.845588-0.6233810.532017-1.911534-1.2160010.780758Category_7Category_8-0.1992171.0
6-0.6189343.2819181.958590-1.594125-1.4363942.3876901.033312Category_10Category_9-2.0499657.0
70.267885-0.0341300.6799991.2395790.4526201.5074470.682192Category_14Category_50.3954032.0
8-0.739003-0.770828-0.5818421.086043-0.585934-2.475919-1.213898Category_9Category_7-0.7274066.0
9-2.9858080.4814141.148295-1.657999-0.5899940.4731260.452621Category_9Category_10-1.6591499.0
Atmospheric DensitySurface TemperatureGravityWater ContentMineral AbundanceOrbital PeriodProximity to StarMagnetic Field StrengthRadiation LevelsAtmospheric Composition IndexPrediction
34049-0.4740321.3878002.2573780.366264-2.2063582.037802-0.259079Category_12Category_10-1.0102077.0
34050-1.0957680.0375361.749769-0.9763900.1418600.868424-1.662002Category_5Category_7-0.0701208.0
34051-1.2534171.2304521.215167-0.936899-1.0832040.103552-0.578476Category_8Category_8-2.0959859.0
34052-1.267488-0.3410910.9826310.578921-2.124749-1.180375-1.712756Category_12Category_9-0.1588465.0
34053-3.096511-1.2613730.1185731.170971-1.5090091.605984-2.165920Category_9Category_9-0.0376371.0
34054-0.316003-1.1605190.544548-1.4071231.4278610.849849-1.932329Category_8Category_51.3337608.0
340550.789506-2.645345-0.375569-2.5799660.7831950.671547-2.041189Category_14Category_40.1705053.0
34056-0.6625630.642230-1.175106-2.783240-0.902704-1.694373-1.824274Category_8Category_81.0103115.0
340570.475118-0.0214582.0862741.444825-1.986595-2.113147-0.348915Category_11Category_9-0.6653456.0
34058-0.6881730.0932261.882621-0.561177-0.6834090.948743-1.827309Category_8Category_9-0.3818638.0